fleet-memory/hindsight-docs/docs-integrations/ag2.md

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8	AG2 (AutoGen) Persistent Memory with Hindsight | Integration Guide	Add long-term persistent memory to your AG2 (AutoGen) agents with Hindsight. Automatic fact extraction, entity tracking, and recall tools that persist across conversations.

AG2

Persistent long-term memory for AG2 agents (community AutoGen fork). Give your agents retain/recall/reflect tools that persist across conversations.

View Changelog →

Features

• Drop-in Tools — register_hindsight_tools() registers retain, recall, and reflect in one line
• AG2-native — Uses Annotated type hints compatible with AG2's @register_for_llm / @register_for_execution pattern
• GroupChat Support — Multiple agents can share a single memory bank
• Selective Tools — Include only the tools you need (include_retain, include_recall, include_reflect)
• Simple Configuration — Configure once globally or override per tool set

Installation

pip install hindsight-ag2

Quick Start

from autogen import AssistantAgent, UserProxyAgent, LLMConfig
from hindsight_ag2 import register_hindsight_tools

llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")

with llm_config:
    assistant = AssistantAgent(
        name="assistant",
        system_message="You are a helpful assistant with long-term memory.",
    )
    user_proxy = UserProxyAgent(
        name="user",
        human_input_mode="NEVER",
    )

# Register Hindsight memory tools on both agents
register_hindsight_tools(
    assistant, user_proxy,
    bank_id="my-bank",
    hindsight_api_url="http://localhost:8888",
)

# The assistant can now use hindsight_retain, hindsight_recall, hindsight_reflect
result = user_proxy.initiate_chat(
    assistant,
    message="Remember that I prefer Python over JavaScript.",
)

That's it. The assistant can now store and retrieve memories across conversations.

How It Works

The integration provides three AG2-compatible tool functions backed by Hindsight's API:

Tool	Hindsight	What happens
hindsight_retain(content)	retain(bank_id, content, ...)	Content is stored. Hindsight extracts facts, entities, and relationships from the raw text.
hindsight_recall(query)	recall(bank_id, query, ...)	Hindsight runs semantic search, BM25, graph traversal, and reranking. Returns a numbered list of matching memories.
hindsight_reflect(query)	reflect(bank_id, query, ...)	Hindsight synthesizes a reasoned answer from all relevant memories, using the bank's disposition traits.

Tools are plain Python functions with Annotated type hints. AG2 uses these hints to generate the tool schema that the LLM sees.

Configuration

Global Configuration

from hindsight_ag2 import configure

configure(
    hindsight_api_url="http://localhost:8888",
    api_key="your-key",       # or set HINDSIGHT_API_KEY env var
    budget="mid",              # low / mid / high
    max_tokens=4096,
    tags=["source:ag2"],       # default tags for retain
)

Per-Tool Overrides

Constructor arguments override global configuration:

from hindsight_ag2 import create_hindsight_tools

tools = create_hindsight_tools(
    bank_id="my-bank",
    hindsight_api_url="http://localhost:8888",
    budget="high",
    max_tokens=8192,
    tags=["team:alpha"],
)

GroupChat with Shared Memory

Multiple agents can share a single memory bank in a GroupChat:

from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager, LLMConfig
from hindsight_ag2 import register_hindsight_tools

llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")

with llm_config:
    researcher = AssistantAgent(name="researcher", system_message="You research topics.")
    writer = AssistantAgent(name="writer", system_message="You write content.")
    executor = UserProxyAgent(name="executor", human_input_mode="NEVER")

# All agents share the same memory bank
for agent in [researcher, writer]:
    register_hindsight_tools(agent, executor, bank_id="team-memory")

group_chat = GroupChat(agents=[researcher, writer, executor], messages=[])
manager = GroupChatManager(groupchat=group_chat)

Manual Registration

For full control over how tools are registered:

from hindsight_ag2 import create_hindsight_tools

tools = create_hindsight_tools(
    bank_id="my-bank",
    hindsight_api_url="http://localhost:8888",
)
for tool_fn in tools:
    assistant.register_for_llm(description=tool_fn.__doc__)(tool_fn)
    user_proxy.register_for_execution()(tool_fn)

API Reference

Configuration

Function	Description
configure(...)	Set global connection and default settings
get_config()	Get current configuration
reset_config()	Reset configuration to None

create_hindsight_tools

Parameter	Default	Description
bank_id	required	Hindsight memory bank ID
client	None	Pre-configured Hindsight client
hindsight_api_url	from config	Hindsight API URL
api_key	from config	API key
budget	"mid"	Recall/reflect budget (low/mid/high)
max_tokens	4096	Max tokens for recall results
tags	None	Tags applied when storing memories
recall_tags	None	Tags to filter when searching
recall_tags_match	"any"	Tag matching mode (any/all/any_strict/all_strict)
retain_metadata	None	Metadata dict for retain operations
retain_document_id	None	Document ID for retain (groups/upserts memories)
recall_types	None	Fact types to filter (world, experience, opinion, observation)
recall_include_entities	False	Include entity information in recall results
reflect_context	None	Additional context for reflect operations
reflect_max_tokens	max_tokens	Max tokens for reflect results
reflect_response_schema	None	JSON schema to constrain reflect output format
reflect_tags	recall_tags	Tags to filter memories used in reflect
reflect_tags_match	recall_tags_match	Tag matching for reflect
include_retain	True	Include the retain tool
include_recall	True	Include the recall tool
include_reflect	True	Include the reflect tool

Requirements

• Python >= 3.10
• ag2 >= 0.9.0
• A running Hindsight API server